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C500 | There are limits on how smart humans can get, and any increases in thinking ability are likely to come with problems. So most humans top out under six feet. Just as there are evolutionary tradeoffs for physical traits, Hills says, there are tradeoffs for intelligence. | |
C501 | Both ExpressVPN and NordVPN offer multiple VPN protocols. NordVPN has a slight edge because it offers the fast IKEv2 for use with mobile devices. The encryption standard offered by ExpressVPN is slightly better than that of NordVPN. Both companies use AES encryption with a 256-bit key. | |
C502 | NLP is short for natural language processing while NLU is the shorthand for natural language understanding. They share a common goal of making sense of concepts represented in unstructured data, like language, as opposed to structured data like statistics, actions, etc. | |
C503 | Rejection region: z > 1.645, which corresponds to α = 0.05. | |
C504 | "A discrete variable is one that can take on finitely many, or countably infinitely many values", whereas a continuous random variable is one that is not discrete, i.e. "can take on uncountably infinitely many values", such as a spectrum of real numbers. | |
C505 | ANOVA is used to compare and contrast the means of two or more populations. ANCOVA is used to compare one variable in two or more populations while considering other variables. | |
C506 | Lasso tends to do well if there are a small number of significant parameters and the others are close to zero (ergo: when only a few predictors actually influence the response). Ridge works well if there are many large parameters of about the same value (ergo: when most predictors impact the response). | |
C507 | As discussed above, these two tests should be used for different data structures. Two-sample t-test is used when the data of two samples are statistically independent, while the paired t-test is used when data is in the form of matched pairs. | |
C508 | Logistic regression assumes linearity of independent variables and log odds. Although this analysis does not require the dependent and independent variables to be related linearly, it requires that the independent variables are linearly related to the log odds. | |
C509 | The Hidden layer of the neural network is the intermediate layer between Input and Output layer. Activation function applies on hidden layer if it is available. Hidden nodes or hidden neurons are the neurons that are neither in the input layer nor the output layer [3]. | |
C510 | Reactive management is the polar opposite, and usually a follow-up, of proactive management. When a proactive leader gets swarmed enough with problems long enough, they turn reactive. Reactive management is an approach to management when the company leadership cannot or does not plan ahead for potential problems. | |
C511 | Markovian is an adjective that may describe: In probability theory and statistics, subjects named for Andrey Markov: A Markov chain or Markov process, a stochastic model describing a sequence of possible events. The Markov property, the memoryless property of a stochastic process. | |
C512 | One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture. | |
C513 | Ordinary least squares (OLS) is a non-iterative method that fits a model such that the sum-of-squares of differences of observed and predicted values is minimized. Gradient descent finds the linear model parameters iteratively. | |
C514 | Bivariate analysis looks at two paired data sets, studying whether a relationship exists between them. Multivariate analysis uses two or more variables and analyzes which, if any, are correlated with a specific outcome. The goal in the latter case is to determine which variables influence or cause the outcome. | |
C515 | Transfer learning without any labeled data from the target domain is referred to as unsupervised transfer learning. | |
C516 | In an observational study or an experiment, the variable whose values are to be predicted from values of other values is called a response variable and variables whose values are used to predict values of another variable are called predictor variables. | |
C517 | You can use KNN by converting the categorical values into numbers.Enumerate the categorical data, give numbers to the categories, like cat = 1, dog = 2 etc.Perform feature scaling. So that the loss function is not biased to some particular features.Done, now apply the K- nearnest neighbours algorithm. | |
C518 | A psychometric and capability test aims to provide measurable, objective data that can give you a better versatile view of a candidate's skills and suitability for a position. Assessments offer scientific, valid reliable and objectivity to the process of recruiting. | |
C519 | Connected components, in a 2D image, are clusters of pixels with the same value, which are connected to each other through either 4-pixel, or 8-pixel connectivity. We offer several user-friendly ways to segment, and then rapidly calculate and display the connected components of 2D and 3D segmentations. | |
C520 | 9:4220:54Suggested clip · 97 secondsPermutations Combinations Factorials & Probability - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C521 | No. Stock return is not always stationary. Using non-stationary time series data in financial models produces unreliable and spurious results and leads to poor understanding and forecasting. The solution to the problem is to transform the time series data so that it becomes stationary. | |
C522 | The intercept (often labeled the constant) is the expected mean value of Y when all X=0. Start with a regression equation with one predictor, X. If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value. | |
C523 | Multicollinearity is a problem because it undermines the statistical significance of an independent variable. Other things being equal, the larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant. | |
C524 | In general, prediction is the process of determining the magnitude of statistical variates at some future point of time. | |
C525 | follows a negative binomial distribution with parameters r and p. The geometric distribution is a special case of discrete compound Poisson distribution. | |
C526 | Divide the total by the number of members of the cluster. In the example above, 283 divided by four is 70.75, and 213 divided by four is 53.25, so the centroid of the cluster is (70.75, 53.25). | |
C527 | The Poisson regression model introduced above is the most natural example of such a count data regression model. It provides a fully parametric approach and suggests MCMC techniques for fitting a model to the given data. | |
C528 | The cumulative distribution function (CDF) calculates the cumulative probability for a given x-value. Use the CDF to determine the probability that a random observation that is taken from the population will be less than or equal to a certain value. | |
C529 | Betas are calculated by subtracting the mean from the variable and dividing by its standard deviation. This results in standardized variables having a mean of zero and a standard deviation of 1. Standardized beta coefficients are also called: Betas. | |
C530 | The simplest form of language model simply throws away all conditioning context, and estimates each term independently. Such a model is called a unigram language model : (95) There are many more complex kinds of language models, such as bigram language models , which condition on the previous term, (96) | |
C531 | Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. | |
C532 | In the absence of a class label, clustering analysis is also called unsupervised learning, as opposed to supervised learning that includes classification and regression. Accordingly, approaches to clustering analysis are typically quite different from supervised learning. | |
C533 | Nonstandard units provide a good rationale for using standard units. It allows for a good transition into standard units because the students can understand the need for standard units if they have measured the same object but determined differing answers. | |
C534 | The input to multidimensional scaling is a distance matrix. The output is typically a two-dimensional scatterplot, where each of the objects is represented as a point. | |
C535 | three | |
C536 | The training data is an initial set of data used to help a program understand how to apply technologies like neural networks to learn and produce sophisticated results. Training data is also known as a training set, training dataset or learning set. | |
C537 | The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2). The KS test report the maximum difference between the two cumulative distributions, and calculates a P value from that and the sample sizes. | |
C538 | Difference between K Means and Hierarchical clustering Hierarchical clustering can't handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). | |
C539 | Types of predictive modelsForecast models. A forecast model is one of the most common predictive analytics models. Classification models. Outliers Models. Time series model. Clustering Model. The need for massive training datasets. Properly categorising data. | |
C540 | A/B testing (also known as split testing) is the process of comparing two versions of a web page, email, or other marketing asset and measuring the difference in performance. You do this giving one version to one group and the other version to another group. Then you can see how each variation performs. | |
C541 | Rule-based systems process data and output information, but they also process rules and make decisions. Knowledge-based systems also process data and rules to output information and make decisions. In addition, they also process expert knowledge to output answers, recommendations, and expert advice. | |
C542 | For a discrete random variable, the expected value, usually denoted as or , is calculated using: μ = E ( X ) = ∑ x i f ( x i ) | |
C543 | The General Linear Model (GLM) is a useful framework for comparing how several variables affect different continuous variables. In it's simplest form, GLM is described as: Data = Model + Error (Rutherford, 2001, p.3) GLM is the foundation for several statistical tests, including ANOVA, ANCOVA and regression analysis. | |
C544 | In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success/yes/true/one (with probability p) | |
C545 | Content-based recommendation systems uses their knowledge about each product to recommend new ones. Recommendations are based on attributes of the item. Content-based recommender systems work well when descriptive data on the content is provided beforehand. “Similarity” is measured against product attributes. | |
C546 | 1 Answer. 1. 8. Without math: The delta rule uses gradient descent to minimize the error from a perceptron network's weights. Gradient descent is a general algorithm that gradually changes a vector of parameters in order to minimize an objective function. | |
C547 | Preparing Text for Natural Language ProcessingFeature Extraction. Step 1 : Collect Data , for example consider the nursery rhyme. Step 2 : Design the vocabulary , while defining the vocabulary we take the pre-processing text steps as mentioned previously to clean the text of punctuation , converting all words to small case etc. Step 3 : Create Document Vectors.More items• | |
C548 | The error sum of squares is obtained by first computing the mean lifetime of each battery type. For each battery of a specified type, the mean is subtracted from each individual battery's lifetime and then squared. The sum of these squared terms for all battery types equals the SSE. SSE is a measure of sampling error. | |
C549 | For most common clustering software, the default distance measure is the Euclidean distance. Correlation-based distance considers two objects to be similar if their features are highly correlated, even though the observed values may be far apart in terms of Euclidean distance. | |
C550 | Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). | |
C551 | To calculate probabilities involving two random variables X and Y such as P(X > 0 and Y ≤ 0), we need the joint distribution of X and Y . The way we represent the joint distribution depends on whether the random variables are discrete or continuous. p(x,y) = P(X = x and Y = y),x ∈ RX ,y ∈ RY . | |
C552 | Since a Naive Bayes text classifier is based on the Bayes's Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful. | |
C553 | Kurtosis is the characteristic of being flat or peaked. It is a measure of whether data is heavy-tailed or light-tailed in a normal distribution. | |
C554 | The S-curve shows the innovation from its slow early beginnings as the technology or process is developed, to an acceleration phase (a steeper line) as it matures and, finally, to its stabilisation over time (the flattening curve), with corresponding increases in performance of the item or organisation using it. | |
C555 | Since p < 0.05 is enough to reject the null hypothesis (no association), p = 0.002 reinforce that rejection only. If the significance value that is p-value associated with chi-square statistics is 0.002, there is very strong evidence of rejecting the null hypothesis of no fit. It means good fit. | |
C556 | A feedforward neural network is a biologically inspired classification algorithm. It consist of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer. This is why they are called feedforward neural networks. | |
C557 | The total entropy of a system either increases or remains constant in any process; it never decreases. For example, heat transfer cannot occur spontaneously from cold to hot, because entropy would decrease. Entropy is very different from energy. Entropy is not conserved but increases in all real processes. | |
C558 | Specifical- ly, for periodic signals we can define the Fourier transform as an impulse train with the impulses occurring at integer multiples of the fundamental frequency and with amplitudes equal to 27r times the Fourier series coefficients. | |
C559 | Groupthink can lead collective rationalization, lack of personal accountability and pressure to acquiesce. Groupthink is a common factor in bad decision-making and serious ethical breaches. They take precautions to prevent groupthink from taking hold. | |
C560 | The Antardasha of Mercury with Ketu Mahadasha can be evil and good depending on the placement of both Mercury and Ketu in the birth chart. The antardasha of Mercury with Mahadasha of Ketu brings very bad results if the planet Mercury is weak, afflicted, aspect by Rahu, Saturn and Mars. | |
C561 | Some of the algorithms used in image recognition (Object Recognition, Face Recognition) are SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), PCA (Principal Component Analysis), and LDA (Linear Discriminant Analysis). | |
C562 | Thus logit regression is simply the GLM when describing it in terms of its link function, and logistic regression describes the GLM in terms of its activation function. | |
C563 | Leaky ReLU & Parametric ReLU (PReLU) Leaky ReLU has two benefits: It fixes the “dying ReLU” problem, as it doesn't have zero-slope parts. It speeds up training. There is evidence that having the “mean activation” be close to 0 makes training faster. | |
C564 | If the absolute value of the t-value is greater than the critical value, you reject the null hypothesis. If the absolute value of the t-value is less than the critical value, you fail to reject the null hypothesis. | |
C565 | Birst employs caching and aggregate awareness to send queries to the cache first, and then data to the user-ready data store. If data is not cached, Birst generates one or more queries depending on how the data is sourced. Birst's in-memory caching includes both exact and fuzzy matching. | |
C566 | Extended Kalman filter (EKF): While the Kalman filter is designed for linear discrete-time dynamical system, EKF works for discrete-time nonlinear systems. | |
C567 | User-Based Collaborative Filtering is a technique used to predict the items that a user might like on the basis of ratings given to that item by the other users who have similar taste with that of the target user. Many websites use collaborative filtering for building their recommendation system. | |
C568 | In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. | |
C569 | The probability of a specific value of a continuous random variable will be zero because the area under a point is zero. | |
C570 | The accuracy is a measure of the degree of closeness of a measured or calculated value to its actual value. The percent error is the ratio of the error to the actual value multiplied by 100. The precision of a measurement is a measure of the reproducibility of a set of measurements. A systematic error is human error. | |
C571 | Linear Regression Is Limited to Linear Relationships By its nature, linear regression only looks at linear relationships between dependent and independent variables. That is, it assumes there is a straight-line relationship between them. Sometimes this is incorrect. | |
C572 | You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalise. | |
C573 | A path coefficient is interpreted: If X changes by one standard deviation Y changes by b standard deviations (with b beeing the path coefficient). Dr. Jan-Michael Becker, University of Cologne, SmartPLS Developer. Researchgate: https://www.researchgate.net/profile/Jan_Michael_Becker. | |
C574 | The main use of F-distribution is to test whether two independent samples have been drawn for the normal populations with the same variance, or if two independent estimates of the population variance are homogeneous or not, since it is often desirable to compare two variances rather than two averages. | |
C575 | In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. | |
C576 | Suggest Edits. Support Vector Machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. | |
C577 | In what might only be perceived as a win for Facebook, OpenAI today announced that it will migrate to the social network's PyTorch machine learning framework in future projects, eschewing Google's long-in-the-tooth TensorFlow platform. | |
C578 | If x(n), y(n) and z(n) are the samples of the signals, the correlation coefficient between x and y is given by Sigma x(n) * y(n) divided by the root of [Sigma x(n)^2 * y(n)^2], where ' * ' denotes simple multiplication and ^2 denotes squaring. The summation is taken over all the samples of the signals. | |
C579 | Again, random forest is very effective on a wide range of problems, but like bagging, performance of the standard algorithm is not great on imbalanced classification problems. | |
C580 | Ideally you have some kind of pre-clustered data (supervised learning) and test the results of your clustering algorithm on that. Simply count the number of correct classifications divided by the total number of classifications performed to get an accuracy score. | |
C581 | Nonlinear regression can fit many more types of curves, but it can require more effort both to find the best fit and to interpret the role of the independent variables. Additionally, R-squared is not valid for nonlinear regression, and it is impossible to calculate p-values for the parameter estimates. | |
C582 | Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future data trends [8]. Classification is a data mining (machine learning) technique used to predict group membership for data instances. | |
C583 | The t distributions were discovered by William S. Gosset was a statistician employed by the Guinness brewing company which had stipulated that he not publish under his own name. He therefore wrote under the pen name ``Student. '' These distributions arise in the following situation. | |
C584 | 1. Getting Data from Twitter Streaming APICreate a twitter account if you do not already have one.Click "Create New App"Fill out the form, agree to the terms, and click "Create your Twitter application"In the next page, click on "API keys" tab, and copy your "API key" and "API secret".More items• | |
C585 | In calculating a simple average, or arithmetic mean, all numbers are treated equally and assigned equal weight. But a weighted average assigns weights that determine in advance the relative importance of each data point. | |
C586 | The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data. | |
C587 | The chief difference between MEMM and CRF is that MEMM is locally renormalized and suffers from the label bias problem, while CRFs are globally renormalized. | |
C588 | Calculate bias by finding the difference between an estimate and the actual value. To find the bias of a method, perform many estimates, and add up the errors in each estimate compared to the real value. Dividing by the number of estimates gives the bias of the method. | |
C589 | Definition: Gamma distribution is a distribution that arises naturally in processes for which the waiting times between events are relevant. It can be thought of as a waiting time between Poisson distributed events. | |
C590 | Bayesian theory calls for the use of the posterior predictive distribution to do predictive inference, i.e., to predict the distribution of a new, unobserved data point. Both types of predictive distributions have the form of a compound probability distribution (as does the marginal likelihood). | |
C591 | m (the greek letter "mu") is used to denote the population mean. The population mean is worked out in exactly the same way as the sample mean: add all of the scores together, and divide the result by the total number of scores. In journal articles, the mean is usually represented by M, and the median by Mdn. | |
C592 | Divergent Thinking. By contrast, divergent means “developing in different directions” and so divergent thinking opens your mind in all directions. | |
C593 | Get startedPrepare your TensorBoard logs. (or download a sample from here).Upload the logs. Install the latest version of TensorBoard to use the uploader. $ pip install -U tensorboard. View your experiment on TensorBoard. dev. Follow the link provided to view your experiment, or share it with others. | |
C594 | more A symbol for a value we don't know yet. It is usually a letter like x or y. Example: in x + 2 = 6, x is the variable. | |
C595 | The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. | |
C596 | In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the | |
C597 | Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning, fully supervised learning, and reinforcement learning. | |
C598 | Factorials are symbolized by exclamation points (!). A factorial is a mathematical operation in which you multiple the given number by all of the positive whole numbers less than it. In other words. = n × ( n − 1 ) × … × 2 × 1 . | |
C599 | Linear algebra is called linear because it is the study of straight lines. A linear function is any function that graphs to a straight line, and linear algebra is the mathematics for solving systems that are modeled with multiple linear functions. Multiple linear equations can be expressed as vectors and matrices. |
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